Hydroxyl radical reactivity at the air-ice interface

Hydroxyl radicals are important oxidants in the atmosphere and in natural waters. They are also expected to be important in snow and ice, but their reactivity has not been widely studied in frozen aqueous solution. We have developed a spectroscopic probe to monitor the formation and reactions of hydroxyl radicals in situ. Hydroxyl radicals are produced in aqueous solution via the photolysis of nitrite, nitrate, and hydrogen peroxide, and react rapidly with benzene to form phenol. Similar phenol formation rates were observed in aqueous solution and bulk ice. However, no reaction was observed at air-ice interfaces, or when bulk ice samples were crushed prior to photolysis to increase their surface area. We also monitored the heterogeneous reaction between benzene present at air-water and air-ice interfaces with gas-phase OH produced from HONO photolysis. Rapid phenol formation was observed on water surfaces, but no reaction was observed at the surface of ice. Under the same conditions, we observed rapid loss of the polycyclic aromatic hydrocarbon (PAH) anthracene at air-water interfaces, but no loss was observed at air-ice interfaces. Our results suggest that the reactivity of hydroxyl radicals toward aromatic organics is similar in bulk ice samples and in aqueous solution, but is significantly suppressed in the quasi-liquid layer (QLL) that exists at air-ice interfaces

Conservation agriculture/farmers mechanization: Africa RISING science, innovations and technologies with scaling potential from the Ethiopian Highlands

United States Agency for International Developmen

Coping with Persistent Pain, Effectiveness Research into Self-management (COPERS): statistical analysis plan for a randomised controlled trial

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated

Different photolysis kinetics at the surface of frozen freshwater vs. frozen salt solutions

Reactions at air-ice interfaces can proceed at very different rates than those in aqueous solution, due to the unique disordered region at the ice surface known as the quasi-liquid layer (QLL) . The physical and chemical nature of the surfacial region of ice is greatly affected by solutes such as sodium halide salts. In this work, we studied the effects of sodium chloride and sodium bromide on the photolysis kinetics of harmine, an aromatic organic compound, in aqueous solution and at the surface of frozen salt solutions above the eutectic temperature. In common with other aromatic organic compounds we have studied, harmine photolysis is much faster on ice surfaces than in aqueous solution, but the presence of NaCl or NaBr – which does not affect photolysis kinetics in solution – reduces the photolysis rate on ice. The rate decreases monotonically with increasing salt concentration; at the concentrations found in seawater, harmine photolysis at the surface of frozen salt solutions proceeds at the same rate as in aqueous solution. These results suggest that the brine excluded to the surfaces of frozen salt solutions is a true aqueous solution, and so it may be possible to use aqueous-phase kinetics to predict photolysis rates at sea ice surfaces. This is in marked contrast to the result at the surface of frozen freshwater samples, where reaction kinetics are often not well-described by aqueous-phase processes

Accounting for centre-effects in multicentre trials with a binary outcome - when, why, and how?

BACKGROUND: It is often desirable to account for centre-effects in the analysis of multicentre randomised trials, however it is unclear which analysis methods are best in trials with a binary outcome. METHODS: We compared the performance of four methods of analysis (fixed-effects models, random-effects models, generalised estimating equations (GEE), and Mantel-Haenszel) using a re-analysis of a previously reported randomised trial (MIST2) and a large simulation study. RESULTS: The re-analysis of MIST2 found that fixed-effects and Mantel-Haenszel led to many patients being dropped from the analysis due to over-stratification (up to 69% dropped for Mantel-Haenszel, and up to 33% dropped for fixed-effects). Conversely, random-effects and GEE included all patients in the analysis, however GEE did not reach convergence. Estimated treatment effects and p-values were highly variable across different analysis methods. The simulation study found that most methods of analysis performed well with a small number of centres. With a large number of centres, fixed-effects led to biased estimates and inflated type I error rates in many situations, and Mantel-Haenszel lost power compared to other analysis methods in some situations. Conversely, both random-effects and GEE gave nominal type I error rates and good power across all scenarios, and were usually as good as or better than either fixed-effects or Mantel-Haenszel. However, this was only true for GEEs with non-robust standard errors (SEs); using a robust ‘sandwich’ estimator led to inflated type I error rates across most scenarios. CONCLUSIONS: With a small number of centres, we recommend the use of fixed-effects, random-effects, or GEE with non-robust SEs. Random-effects and GEE with non-robust SEs should be used with a moderate or large number of centres

Allograft and Xenograft Acceptance under FK‐506 and Other Immunosuppressant Treatment

We will focus on two issues, both involving, but not confined to FK-506: first, the meaning of the graft acceptance, which is, after all, the objective of immunosuppression for the transplant surgeon; and second, how to take the next great step of xenotransplantation

Core pinning by intragranular nanoprecipitates in polycrystalline MgCNi_3

The nanostructure and magnetic properties of polycrystalline MgCNi_3 were studied by x-ray diffraction, electron microscopy, and vibrating sample magnetometry. While the bulk flux-pinning force curve F_p(H) indicates the expected grain-boundary pinning mechanism just below T_c = 7.2 K, a systematic change to pinning by a nanometer-scale distribution of core pinning sites is indicated by a shift of F_p(H) with decreasing temperature. The lack of scaling of F_p(H) suggests the presence of 10 to 20% of nonsuperconducting regions inside the grains, which are smaller than the diameter of fluxon cores 2xi at high temperature and become effective with decreasing temperature when xi(T) approaches the nanostructural scale. Transmission electron microscopy revealed cubic and graphite nanoprecipitates with 2 to 5 nm size, consistent with the above hypothesis since xi(0) = 6 nm. High critical current densities, more than 10^6 A/cm^2 at 1 T and 4.2 K, were obtained for grain colonies separated by carbon. Dirty-limit behavior seen in previous studies may be tied to electron scattering by the precipitates, indicating the possibility that strong core pinning might be combined with a technologically useful upper critical field if versions of MgCNi_3 with higher T_c can be found.Comment: 5 pages, 6 figures, submitted to PR

Risk of selection bias in randomised trials

Background: Selection bias occurs when recruiters selectively enrol patients into the trial based on what the next treatment allocation is likely to be. This can occur even if appropriate allocation concealment is used if recruiters can guess the next treatment assignment with some degree of accuracy. This typically occurs in unblinded trials when restricted randomisation is implemented to force the number of patients in each arm or within each centre to be the same. Several methods to reduce the risk of selection bias have been suggested; however, it is unclear how often these techniques are used in practice. Methods: We performed a review of published trials which were not blinded to assess whether they utilised methods for reducing the risk of selection bias. We assessed the following techniques: (a) blinding of recruiters; (b) use of simple randomisation; (c) avoidance of stratification by site when restricted randomisation is used; (d) avoidance of permuted blocks if stratification by site is used; and (e) incorporation of prognostic covariates into the randomisation procedure when restricted randomisation is used. We included parallel group, individually randomised phase III trials published in four general medical journals (BMJ, Journal of the American Medical Association, The Lancet, and New England Journal of Medicine) in 2010. Results: We identified 152 eligible trials. Most trials (98%) provided no information on whether recruiters were blind to previous treatment allocations. Only 3% of trials used simple randomisation; 63% used some form of restricted randomisation, and 35% did not state the method of randomisation. Overall, 44% of trials were stratified by site of recruitment; 27% were not, and 29% did not report this information. Most trials that did stratify by site of recruitment used permuted blocks (58%), and only 15% reported using random block sizes. Many trials that used restricted randomisation also included prognostic covariates in the randomisation procedure (56%). Conclusions: The risk of selection bias could not be ascertained for most trials due to poor reporting. Many trials which did provide details on the randomisation procedure were at risk of selection bias due to a poorly chosen randomisation methods. Techniques to reduce the risk of selection bias should be more widely implemented

Choosing sensitivity analyses for randomised trials: principles

Background Sensitivity analyses are an important tool for understanding the extent to which the results of randomised trials depend upon the assumptions of the analysis. There is currently no guidance governing the choice of sensitivity analyses. Discussion We provide a principled approach to choosing sensitivity analyses through the consideration of the following questions: 1) Does the proposed sensitivity analysis address the same question as the primary analysis? 2) Is it possible for the proposed sensitivity analysis to return a different result to the primary analysis? 3) If the results do differ, is there any uncertainty as to which will be believed? Answering all of these questions in the affirmative will help researchers to identify relevant sensitivity analyses. Treating analyses as sensitivity analyses when one or more of the answers are negative can be misleading and confuse the interpretation of studies. The value of these questions is illustrated with several examples. Summary By removing unreasonable analyses that might have been performed, these questions will lead to relevant sensitivity analyses, which help to assess the robustness of trial results